
Class-specific attention learning with center loss: The center loss which was first introduced in [1]}, learns the class-specific centers and penalizes the distance between the features and their class centers. Narayan et al. in 3C-Net [2]} employed center loss to enhance the feature discriminability and reduce the intra-class variations. For each video \(i\)  and each action class \(c\) , center loss computes the distance (L2 norm) between class-specific foreground feature \(f^i_c\)  (def REF ) and cluster center feature \(z_c\)  as the following:
\(\mathcal {L}_{center} = \frac{1}{N} \sum _{i} \sum _{c : y^i(c) =1} \left\Vert f^i_c - z_c\right\Vert ^2_2,\) 
